Glossary Key Terms and Concepts in NLP and Sentient Computing

Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems, enabling them to perform tasks that typically require human intelligence.

Natural Language Processing (NLP): A branch of AI focused on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and useful.

Deep Learning: A subset of machine learning that uses neural networks with many layers (deep neural networks) to analyze various factors of data with a structure similar to the human neural system.

Transformer Models: A type of deep learning model introduced in the paper "Attention is All You Need," known for its effectiveness in handling sequential data and its use in state-of-the-art NLP tasks.

GPT (Generative Pre-trained Transformer): A type of Transformer model designed to generate text. It is pre-trained on a large corpus of text and then fine-tuned for specific tasks.

BERT (Bidirectional Encoder Representations from Transformers): Another type of Transformer model that is pre-trained on a large corpus of text and designed to understand the context of words in search queries and other text-processing tasks by considering the words that come before and after.

Tokenization: The process of converting text into smaller units, such as words or phrases, for easier processing and analysis in NLP.

Sentiment Analysis: An NLP technique used to determine the attitude or emotional tone behind a body of text, categorizing it as positive, negative, or neutral.

Machine Translation: The use of software to translate text or speech from one language to another, aiming to convey the original tone, style, and meaning as closely as possible.

Speech Recognition: A technology that converts spoken words into text, allowing computers to understand and process human speech.

Text-to-Speech (TTS): A technology that converts text into synthetic speech, enabling computers to read out text in a human-like voice.

Multimodal NLP: An approach in NLP that involves processing and integrating multiple types of data inputs, such as text, audio, and visual data, to understand and generate human language more effectively.

Explainable AI (XAI): AI systems designed in a way that their decisions and actions can be easily understood by humans, enhancing transparency and trust.

Bias in AI: Systematic and unfair discrimination in the outcomes of AI systems, often resulting from biased data sets or algorithms.

Privacy by Design: An approach to systems engineering which takes privacy into account throughout the whole engineering process, ensuring that privacy and data protection are foundational aspects of technology.

Federated Learning: A machine learning approach where the model is trained across multiple decentralized devices or servers holding local data samples, without exchanging them, enhancing privacy and data security.